The journal of knowledge engineering special issue on WorldCist'19—Seventh World Conference on Information Systems and Technologies

1 GUEST EDITORIAL

The constant growth of technology leads to the development of expert systems that serve to support critical decision-making and have applications in many areas, such as healthcare, business, chemistry, financial decision-making, and engineering. These systems are computer programs derived from a computer science research branch called Artificial Intelligence (AI) and use human knowledge intensively in problem-solving. These programs combine expert knowledge and use the knowledge necessary to solve problems (Kidd, 2012). In this special issue, we present a range of papers covering some of the subareas of expert systems such as intelligent and decision support systems, ethics, computers, and security, health informatics, simulations, and big-data analytics.

This special issue comprises six research papers. All manuscripts are extended versions of selected papers from WorldCIST'19 - 7th World Conference on Information Systems and Technologies, held in at La Toja Island, Galicia, Spain, April 2019. The WorldCIST conference have become a global forum for researchers and practitioners to present and discuss the most recent innovations, trends, results, experiences, and concerns in the several perspectives of Information Systems and Technologies, as well as computer science in general. The six selected papers in this special section include a Virtual Programming Lab (VPL), a model's predictions, a novel information systems architecture for the agri-food sector, various approaches for detection of malware, an intelligent system to assess, in real-time, potential HRV indices, that can predict HRQoL in lymphoma patients throughout chemotherapy treatment, as well as an expert system comprising a self-aware framework for resource-efficient and accurate data transmission within a low-power lossy sensor network (LLN) deployed for indoor monitoring.

Cardoso et al. (2020) present the VPL, a Moodle plugin that allows students to submit their code and get prompt feedback without the teacher's intervention. To test this concept, an experiment was performed with several classes of beginner programming students, in two editions of Algorithms and Programming course unit of the degree in Informatics Engineering lectured at the Informatics Engineering Department at the School of Engineering, Polytechnic Institute of Porto. The students were challenged to test their assignments in VPL with a set of test values previously defined by the teachers. After the experiments, the authors used surveys to gather the involved students' and teachers' opinion, and more than 70% of the students answered that they considered the VPL an added value for the teaching–learning process. The dynamics verified in the classes, the general opinion of the teachers, and the acceptance and participation of the students allow to classify the experience as positive.

Almomani et al. (2020) have analysed the decision-making process underlying choice behaviour. First, neural and gaze activity were recorded experimentally from different subjects performing a choice task in a Web Interface. Second, choice models and ensembles were fitted using rational, emotional, and attentional features. The model's predictions were evaluated in terms of their accuracy and rankings were made for each user. The results show that (a) the attentional models are the best in terms of its average performance across all users, (b) each subject shows a different best model, and (c) ensembles may perform better than single choice models but an optimal building method must be found.

Branco et al. (2020) have shown the information systems and technologies grow in usage in the agri-food industry, the same has happened to the relevance of Information Systems (IS) that allow for a parallel control, monitoring and management of the organizations' activities and business processes. As the literature proves, the benefits of implementing adequate and interoperable IS are very numerous and tend to represent a significant determinant regarding the organizations' overall success. In this paper the authors present a novel information systems architecture for the agri-food sector. The artefact is composed by 12 integrated main components and a set of subcomponents aimed at supporting all the monitoring, control, and management activities. To validate the proposed architecture a case study was implemented at a mushroom production organization. This allowed to perceive the ability of the artefact to serve as the basis for the development of IS that address all the organization's business and environmental needs.

Jan et al. (2020) have shown that malware analysis and detection over the Android have been the focus of considerable research, during recent years, as customer adoption of Android attracted a corresponding number of malware writers. Antivirus companies commonly rely on signatures and are error-prone. Traditional machine learning techniques are based on static, dynamic, and hybrid analysis; however, for large scale Android malware analysis, these approaches are not feasible. Deep neural architectures can analyze large scale static details of the applications, but static analysis techniques can ignore many malicious behaviours of applications. The study contributes to the documentation of various approaches for detection of malware, traditional and state-of-the-art models, developed for analysis that facilitates the provision of basic insights for researchers working in malware analysis, and the study also provides a dynamic approach that employs deep neural network models for detection of malware. Moreover, the study uses Android permissions as a parameter to measure the dynamic behaviour of around 16,900 benign and intruded applications. A dataset is created which encompasses a large set of permissions-based dynamic behaviour pertaining applications, with an aim to train deep learning models for prediction of behaviour. The proposed architecture extracts representations from input sequence data with no human intervention. The state-of-the-art Deep Convolutional Generative Adversarial Network extracted deep features and accomplished a general validation accuracy of 97.08% with an F1-score of 0.973 in correctly classifying input. Furthermore, the concept of blockchain is utilized to preserve the integrity of the dataset and the results of the analysis.

Oliveira et al. (2020) presents an intelligent system to assess, in real-time, potential HRV indices, that can predict HRQoL in lymphoma patients throughout chemotherapy treatment and to account the individuals' variability. The system is based on wearable technology and intelligent processing of the patients' biometric information to assess some quality-of-life related parameters. A longitudinal study was conducted among 16 lymphoma patients using this intelligent system. Mixed-effect regression models were performed to investigate predictors for and time effects on HRQoL. There were no significant changes in all HRQoL domains over time. Some quality-of-life domains revealed similar time trends as HRV indices. These HRV indices also have a significant effect on the domains of quality of life.

Habib et al. (2020) have developed an expert system comprising a self-aware framework for resource-efficient and accurate data transmission within a low-power LLN deployed for indoor monitoring. We derived both individual and group awareness, which could ensure the awareness of each sensor regarding its resources, neighbours and network environment. The proposed expert system facilitates decision-making under dynamic environmental conditions and employs a multi-criteria decision-making (MCDM) model to determine the selection of the best path towards the sink node with awareness of the existing network environment. The proposed system is validated by constructing a 6LoWPAN network in the Contiki Cooja simulator. MCDM is applied to generate an adaptive objective function for the IPv6 routing protocol for the LLN (RPL) and to aid in ranking the nodes to select the best available neighbouring node, while the data accuracy is ensured by the cluster head through data correlation among its associated members. The network performance is assessed by analyzing the packet delivery rate, throughput and energy consumption against varying sensors and by comparing our proposed MCDM-RPL with a standard RPL and a fuzzy-based RPL, where the results show that our framework is found to be better with gains of 13%, 25% and 13%, respectively.

2 GUEST EDITORS

Álvaro Rocha holds the title of Honorary Professor, and holds a D.Sc. in Information Science, Ph.D. in Information Systems and Technologies, M.Sc. in Information Management, and BCs in Computer Science. He is a Professor of Information Systems at the University of Lisbon - ISEG, researcher at the ADVANCE (the ISEG Centre for Advanced Research in Management), and a collaborator researcher at both LIACC (Laboratory of Artificial Intelligence and Computer Science) and CINTESIS (Center for Research in Health Technologies and Information Systems). His main research interests are maturity models, information systems quality, online service quality, requirements engineering, intelligent information systems, e-Government, e-Health, and information technology in education. He is also Vice-Chair of the IEEE Portugal Section Systems, Man, and Cybernetics Society Chapter, and Editor-in-Chief of both JISEM (Journal of Information Systems Engineering & Management) and RISTI (Iberian Journal of Information Systems and Technologies). Moreover, he has served as Vice-Chair of Experts for the European Commission's Horizon 2020 program, and as an Expert at the COST - intergovernmental framework for European Cooperation in Science and Technology, at the Government of Italy's Ministry of Universities and Research, at the Government of Latvia's Ministry of Finance, at the Government of Mexico's National Council of Science and Technology, and at the Government of Polish's National Science Centre.

Fernando Moreira is Head of the Science and Technology Department. He is graduated in Computer Science (1992), M.Sc. in Electronic Engineering (1997) and PhD in Electronic Engineering (2003), both at the Faculty of Engineering of the University of Porto and Habilitation (2018). He is a member of the Science and Technology Department at Portucalense University since 1992, currently as a Full Professor and a visiting professor at the University of Porto Business School. He teaches subjects related to undergraduate and post-graduate studies. He supervises several PhD and M.Sc. students. He is a (co-)author of more than 200 scientific publications with peer-review on national and international journals and conferences. He serves as a member of the Editorial Advisory Board for several journals and books. He organized several special issues from JCR journals. He has already regularly served as a member of Programme and Scientific committees of national and international conferences. He was the MSc in Computation coordinator during the last 10 years. He holds editorial experience, and he is co-editor of several books. He is associated with NSTICC, ACM and IEEE. His principal research areas are mobile computing, ICT in Higher Education, Mobile learning, Social business and Digital transformation. He was awarded Atlas Elsevier Award, April 2019.

Ashwani Kumar Dubey received the AMIE (BE) degree in Electronics and Communication Engineering from the Institutions of Engineers (India) Kolkata, India, M. Tech. degree in Instrumentation and Control Engineering from Maharshi Dayanand University, Rohtak, Haryana, India, in 2007, and Ph. D degree from the Department Electrical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia (A Central Govt. University), New Delhi, India, in 2014. He served Apeejay College of Engineering (Sohna), Gurgaon, India; Applied College of Management and Engineering, Palwal, India; BDES Group of Institutions, Faridabad, Indiaq and GD Goenka World Institute-Lancaster University, UK, Sohna, India at different academic and administrative positions. Presently, he is an Associate Professor in the Department of Electronics and Communication Engineering, Amity School of Engineering and Technology, Amity University, Noida, Uttar Pradesh, India. He has more than 20 years of experience at different positions. Dr. Dubey has filed 20 patents and regularly contributed more than 70 research papers in various reputed International and National Journals/Conferences. He has chaired many IEEE International conferences and workshops. He is a regular reviewer of IEEE Transactions on Instrument and Measurement, IEEE Transactions on Industrial Electronics, IEEE Sensors Journal, IEEE Internet of Things (IoT) Journal and Springer's Wireless Personal Communications. He is the Organizing Chair of IEEE SPIN 2019, IEEE SPIN 2020, and IEEE SPIN 2021 International Conferences. Dr Dubey has organized several International and National webinars and seminars. He has reviewed a book “Biosensors: An Introductory Textbook” published by Pan Stanford, Singapore. He edited two books namely “Microcontrollers and Embedded Systems” and “Digital Electronics” published by UDH publications, India. He is a corporate member of The Institution of Engineers (India). He had been honoured with “Shikshak Shiromani Award” by Lions Club, Delhi, India in 2011 and as an “Eminent Speaker” in “Technology based Entrepreneurship Development Programme (TEDP) 2016” sponsored by “National Science & Tech. Entrepreneurship Development Board, DST, Govt of India, New Delhi”, and “Entrepreneurship Development Institute of India, Ahmedabad, Gujarat, India in 2016”. He has developed many visual inspection systems for various industrial and agricultural applications to improve and monitor the quality and reliability of the products/systems. He has guided three PhD research scholars and presently guiding six PhD research scholars. His research works includes wireless sensor networks, digital image processing, medical image processing, food, and fruit quality inspections through computer vision, MEMS/ NEMS based smart sensors and systems, etc.